Exploiting Object Similarity in 3D Reconstruction

Chen Zhou, Fatma Guney, Yizhou Wang, Andreas Geiger; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2201-2209


Despite recent progress, reconstructing outdoor scenes in 3D from movable platforms remains a highly difficult endeavour. Challenges include low frame rates, occlusions, large distortions and difficult lighting conditions. In this paper, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by localizing objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows us to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. We evaluate our approach with respect to LIDAR ground truth on a novel challenging suburban dataset and show its advantages over the state-of-the-art.

Related Material

author = {Zhou, Chen and Guney, Fatma and Wang, Yizhou and Geiger, Andreas},
title = {Exploiting Object Similarity in 3D Reconstruction},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}